In our first ablation experiment, we have eliminated all the enhancements in order to show the performance level of a baseline CNN on the CH data set. Since there are several SISR works [1]}, [2]}, [3]}, [4]} based on the standard ESPCN model [5]}, we have eliminated the second convolutional block in the second ablation experiment, transforming our architecture into a standard ESPCN architecture. The performance drops from \(0.9270\)  to \(0.9236\)  in terms of SSIM and from \(36.22\)  to \(35.94\)  in terms of PSNR. In the subsequent ablation experiments, we have removed, in turns, the intermediate loss, the short-skip connections and the long-skip connection. The results presented in Table REF  indicate that all these components are relevant to our model, bringing significant performance benefits in terms of both SSIM and PSNR. In our last ablation experiment, we used a fixed standard deviation instead of a variable one for the Gaussian blur added on training patches. We notice that our data augmentation approach based on a variable standard deviation brings the highest gains in terms of SSIM (from \(0.9236\)  to \(0.9270\) ) and PSNR (from \(35.69\)  to \(36.22\) ), with respect to the other ablated components.
